1. Field of the Invention
The present invention relates to a smart antenna system and, in particular, to an improved adaptive beamforming method for an antenna array in a Code Division Multiple Access (CDMA) communication system.
2. Background of the Related Art
In wireless communication systems, various diverse methods are used for increasing the coverage area and capacity of the system.
Rake receiver architecture provides an effective immunity to the inter-symbol interference (ISI) in multipath propagation environments, which cause the same signal to be repeatedly received at an antenna at a plurality of different time intervals.
Recently, directive antennas have been employed to increase the signal-to-interference plus noise ratio (SINR) by increasing the energy radiated to a desired mobile terminal, while simultaneously reducing the interference energy radiated to other remote mobile terminals. Such reduction in the interference energy radiated to mobile terminals can be achieved by generating spatially selective, directive transmission beam patterns.
One directive antenna technique is adaptive beamforming, in which the beam pattern produced by beamforming antenna arrays of the base station adapts in response to changing multipath conditions. In such beamforming arrays, weight vectors are used to generate on antenna beam pattern that maximizes signal energy transmitted to and received from an intended mobile terminal.
There are a number of algorithms presently in use for calculating the weight vectors. These algorithms rely on adjusting the weight vector so as to track the more slowly changing components of the received signal and assume that more rapidly changing random signal components are removed by integration and are hence not tracked.
These algorithms update the weight vector based on a generalized eigenvalue problem, and the generalized eigenvalue problem is converted so as to be an ordinary eigenvalue problem. Continuously, a positive definite matrix is taken among two matrixes consisting of the generalized eigenvalue problem, and the positive definite matrix is written with two matrixes such that an inverse matrix should be obtained from one of the two written matrixes.
However, the conventional algorithms have a drawback in that the weight vector calculation is so complicated and time-consuming that it is not appropriate for an array antenna system that requires real time environment adaptation.
An object of the invention is to solve at least the above problems and/or disadvantages and to provide at least the advantages described hereinafter.
It is an object of the present invention to provide an adaptive beamforming method capable of reducing total computational load for computing weight vector.
It is another object of the present invention to provide an adaptive beamforming method capable of maximizing the Signal to Interference plus Noise (SINR).
To achieve the above objects, the adaptive beamforming method of the present invention comprises the steps of setting an initial weight vector w; updating present autocovariance matrixes Rxx and Ryy of the generalized eigenvalue problem with the signal vectors y and x at a present snapshot; obtaining diagonal and off-diagonal matrixes of one of the autocovariance matrixes Rxx and Ryy; computing the maximum eigenvalue xcex using the weight vector w, the autocovariance matrixes Rxx and Ryy at the present snapshot, and the diagonal and off-diagonal matrixes; and updating the weight vector using the present weight vector w, the eigenvalue xcex, and autocovariance matrixes Rxx and Ryy.
The diagonal matrix is a matrix whose diagonal elements are identical to diagonal elements of a square matrix and whose off-diagonal elements are zero, and the off-diagonal matrix is a matrix whose diagonal elements are zero and whose off-diagonal elements are identical to off-diagonal elements of the square matrix.
In one aspect of the present invention, the diagonal and off-diagonal matrixes are RxxD and RxxO derived from the autocovariance matrix Rxx.
The maximum eigenvalue xcex is calculated in accordance with the following equation:       λ    =                                        w            _                    H                ⁢                              R                          _              _                                yy                ⁢                  xe2x80x83                ⁢                  w          _                                                  w            _                    H                ⁢                              R                          _              _                                xx                ⁢                  xe2x80x83                ⁢                  w          _                      ⁢      xe2x80x83  
where H is the Hermitian operator.
The weight vector w is updated in accordance with the following equation:             w      _        ⁢          (              k        +        1            )        =                    [                                                                              R                                      _                    _                                                  yy                            ⁢                              (                k                )                                      ⁢                                          (                                                                            R                                              _                        _                                                              xx                    D                                    ⁢                                      (                    k                    )                                                  )                                            -                1                                              -                      λ            ⁢                                                            R                                      _                    _                                                  xx                O                            ⁢                              (                k                )                                      ⁢                                          (                                                                            R                                              _                        _                                                              xx                    D                                    ⁢                                      (                    k                    )                                                  )                                            -                1                                                    ]            ⁢                        w          _                ⁢                  (          k          )                      λ  
where k is a snapshot index for receiving the signal vector and for updating the weight vector.
The maximum eigenvalue xcex(k) at the present snapshot is computed in accordance with the following equation:       λ    ⁢          (      k      )        =                    λ        nom            ⁢              (        k        )                            λ        den            ⁢              (        k        )            
where nom denotes numerator and den denotes denominator,
xcexnom(k)=ƒxcexnom(kxe2x88x921)+|xcex1(k)|2, and xcexden(k)=ƒxcexden(kxe2x88x921)+|xcex2(k)|2. 
xcex1(k) is obtained in accordance with xcex1(k)=yH(k)w(k), and xcex2(k) is obtained in accordance with xcex2(k)=xH(k)w(k).
The weight vector is updated in accordance with the following equation:
w(k+1)=w(k)+o(k), 
where             o      _        ⁡          (      k      )        =                              [                                                    R                                  _                  _                                            xx              D                        ⁡                          (              k              )                                ]                          -          1                    ⁡              [                                            1                              λ                ⁢                                  xe2x80x83                                ⁢                                  (                  k                  )                                                      ⁢                                          v                _                            ⁡                              (                k                )                                              -                                    q              _                        ⁡                          (              k              )                                      ]              .  
v(k) is obtained in accordance with the following equation:                                           v            _                    ⁡                      (            k            )                          =                ⁢                              f            ⁢                                                            R                                      _                    _                                                  yy                            ⁡                              (                                  k                  -                  1                                )                                      ⁢                                          w                _                            ⁡                              (                k                )                                              +                                    y              _                        ⁡                          (              k              )                                +                                                                      y                  _                                H                            ⁡                              (                k                )                                      ⁢                                          w                _                            ⁡                              (                k                )                                                                            ≈                ⁢                              f            ⁢                                                            R                                      _                    _                                                  yy                            ⁡                              (                                  k                  -                  1                                )                                      ⁢                                          w                _                            ⁡                              (                                  k                  -                  1                                )                                              +                                                    y                _                            ⁡                              (                k                )                                      ⁢                                                            y                  _                                H                            ⁡                              (                k                )                                      ⁢                                                            w                  _                                ⁡                                  (                  k                  )                                            .                                                              ≈                ⁢                              f            ⁢                          xe2x80x83                        ⁢                                          v                _                            ⁡                              (                                  k                  -                  1                                )                                              +                                    α              ⁡                              (                k                )                                      ⁢                                                            y                  _                                ⁡                                  (                  k                  )                                            .                                          
q(k) is obtained in accordance with the following equation:                                           q            _                    ⁡                      (            k            )                          =                ⁢                              f            ⁢                                                            R                                      _                    _                                                  xx                            ⁡                              (                                  k                  -                  1                                )                                      ⁢                                          w                _                            ⁡                              (                k                )                                              +                                                    x                _                            ⁡                              (                k                )                                      ⁢                                                            x                  _                                H                            ⁡                              (                k                )                                      ⁢                                                            w                  _                                ⁡                                  (                  k                  )                                            .                                                              ≈                ⁢                              f            ⁢                          xe2x80x83                        ⁢                                          q                _                            ⁡                              (                                  k                  -                  1                                )                                              +                      β            ⁢                          xe2x80x83                        ⁢                          (              k              )                        ⁢                                                            x                  _                                ⁡                                  (                  k                  )                                            .                                          
In another aspect of the present invention, the diagonal and off-diagonal matrixes are RyyD and RyyO derived from the autocovariance matrix Ryy.
The maximum eigenvalue xcex is calculated in accordance with the following equation:       λ    =                                        w            _                    H                ⁢                              R                          _              _                                yy                ⁢                  xe2x80x83                ⁢                  w          _                                                  w            _                    H                ⁢                              R                          _              _                                xx                ⁢                  xe2x80x83                ⁢                  w          _                      ⁢      xe2x80x83  
where H is the Hermitian operator.
The weight vector w is updated in accordance with the following equation:
w(k+1)=w(k)+p(k) 
where p(k)=[RyyD(k)]xe2x88x921[xcex(k)xcex3(k)xe2x88x92xcex7(k)].
xcex3(k) is obtained in accordance with the following equation:                                           γ            _                    ⁡                      (            k            )                          =                ⁢                              f            ⁢                                                            R                                      _                    _                                                  xx                            ⁡                              (                                  k                  -                  1                                )                                      ⁢                                          w                _                            ⁡                              (                k                )                                              +                                                    x                _                            ⁡                              (                k                )                                      ⁢                                                            x                  _                                H                            ⁡                              (                k                )                                      ⁢                                          w                _                            ⁡                              (                k                )                                                                            ≈                ⁢                              f            ⁢                          xe2x80x83                        ⁢                                          γ                _                            ⁡                              (                                  k                  -                  1                                )                                              +                      β            ⁢                          xe2x80x83                        ⁢                          (              k              )                        ⁢                                          x                _                            ⁡                              (                k                )                                                        
where xcex2(k)=xH(k)w(k).
xcex7(k) is obtained in accordance with the following equation:                                           η            _                    ⁡                      (            k            )                          =                ⁢                              f            ⁢                                                            R                                      _                    _                                                  yy                            ⁡                              (                                  k                  -                  1                                )                                      ⁢                                          w                _                            ⁡                              (                k                )                                              +                                                    y                _                            ⁡                              (                k                )                                      ⁢                                                            y                  _                                H                            ⁡                              (                k                )                                      ⁢                                          w                _                            ⁡                              (                k                )                                                                            ≈                ⁢                              f            ⁢                          xe2x80x83                        ⁢                                          η                _                            ⁡                              (                                  k                  -                  1                                )                                              +                      α            ⁢                          xe2x80x83                        ⁢                          (              k              )                        ⁢                                          y                _                            ⁡                              (                k                )                                                        
where xcex1(k)=yH(k)w(k).
To achieve at least the above objects, in whole or in part, there, is provided a method for updating a weight factor for input signals from a plurality of antennas of a wireless communication system, including setting an initial weight vector for weighting the input signals, separating desired signals from the input signals, obtaining auto-covariance matrixes of the separated desired signals and the input signals, respectively, computing an eigenvalue that maximizes a ratio of a vector that is a multiplication of the auto-covariance matrix of the separated desired signals and the initial weight vector over a vector that is a multiplication of the auto-covariance matrix of the input signals and the initial weight vector, generating a new weight vector by adding a vector to the initial weight vector, wherein said added vector is proportional to the multiplication of the inverse of a diagonal matrix of the auto-covariance matrix of the input signals and a vector made from the multiplication of a vector of the separated signals and the initial weight vector and the inverse of the eigenvalue.
To achieve at least these advantages, in whole or in part, there is further provided a method for updating a weight factor for input signals from a plurality of antennas of a wireless communication system, including setting an initial weight vector for weighting the input signals, separating desired signals from the input signals, obtaining auto-covariance matrixes of the separated desired signals and the input signals, respectively, computing an eigenvalue that maximizes a ratio of a vector that is a multiplication of the auto-covariance matrix of the separated desired signals and the initial weight vector over a vector that is a multiplication of the auto-covariance matrix of the input signals and the initial weight vector, generating a new weight vector by adding a vector to the initial weight vector, wherein said added vector is proportional to the multiplication of the inverse of a diagonal matrix of the auto-covariance matrix of the separated desired signals and a vector made from the multiplication of a vector of the input signals and the initial weight vector and the eigenvalue.
To achieve at least these advantages, in whole or in part, there is further provided a method for updating a maximum eigenvalue for generating a weight factor for weighting the signals from a plurality of antennas of a wireless communication system, including setting an initial weight vector for weighting the input signals, separating desired signals from the input signals, generating a first value by multiplying the Hermitian of a vector of the separated desired signals and the weight vector, generating a second value by multiplying the Hermitian of a vector of the input signals, computing a numerator of a new maximum eigenvalue by adding a square of said first value to a portion of a numerator of a previous maximum eigenvalue, computing a denominator of the new maximum eigenvalue by adding a square of said second value to a portion of a denominator of the previous maximum eigenvalue; and replacing the previous maximum eigenvalue with the new maximum eigenvalue having said numerator and denominator.
To achieve at least these advantages, in whole or in part, there is further provided an adaptive beam forming method for a communication receiver that inputs signals from an antenna array, including separating desired signals from the input signals, calculating a maximum eigenvalue by defining it as a portion of a numerator and a portion of a denominator, calculating a weight vector using said maximum eigenvalue, said desired signals, and said input signals, and weighting signals from or to a plurality antennas in said antenna array with the weight vector.
To achieve at least these advantages, in whole or in part, there is further provided a method of obtaining a maximum eigenvalue for deriving a weight vector for weighting input signals from a plurality of antennas, including separating desired signals from the input signals, setting an initial weight vector for weighting the input signals, generating a first value based on a Hermitian of a vector of the separated desired signals, generating a second value based on a Hermitian of a vector of the input signals, and deriving the maximum eigenvalue from the first and second values.
To achieve at least these advantages, in whole or in part, there is further provided a method of updating a weight vector for weighting input signals from a plurality of antennas, including setting an initial weight vector for weighting the input signals, separating desired signals from the input signals, deriving an auto-covariance matrix of the separated desired signals, deriving an auto-covariance matrix of the input signals, deriving a maximum eigenvalue using the auto-covariance matrix of the separated desired signals, the auto-covariance matrix of the input signals and the initial weight vector; and deriving a new weight vector by adding a vector to the initial weight vector, wherein the added vector is based on a vector of the separated signals, a vector of the input signals, the initial weight vector, the maximum eigenvalue, and one of the auto-covariance matrix of the input signals and the auto-covariance matrix of the separated desired signals.
To achieve at least these advantages, in whole or in part, there is further provided an adaptive beam forming method, including receiving input signals from a plurality of antennas, separating desired signals from the input signals, setting an initial weight vector for weighting the input signals, generating a first value based on a Hermitian of a vector of the separated desired signals, generating a second value based on a Hermitian of a vector of the input signals, and deriving the maximum eigenvalue from the first and second values, deriving a weight vector using the separated desired signals, the input signals and the derived maximum eigenvalue, and applying the weight vector to the input signals or to signals being sent to the plurality of antennas.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and advantages of the invention may be realized and attained as particularly pointed out in the appended claims.